Image processing method

ABSTRACT

An image processing method comprises an extracting step for extracting a plurality of images of different depths of a 3-dimensional object, the axes forming different angles with the 3-dimensional object, a binary coding step for eliminating a gray level range of the plurality of images obtained by the extracting step, the gray level range containing a small amount of components of the 3-dimensional object, remapping the other portion to a predetermined gray level value range, and putting the plurality of images in binary form, and a step for forming a 3-dimensional image of the 3-dimensional object based on the plurality of images put in binary form by the binary coding step.

TECHNICAL FIELD

The invention relates to an image processing method and particularly toan image processing method to process 3-dimensional images.

BACKGROUND ART

Recently, with development of a computer technique that is applied tothe medical field, the diagnosis of a human body and structural analysisare commonly performed based on 3-dimensional data obtained by CT andMRI. Additionally, in the field of dentistry, a 3-dimensional imagingtechnique is used for a study to form a 3-dimensional model of thetemporomandibular joint from data filmed by CT, for example, YoshinoriArai, Koji Hashimoto, Hiroshi Shinoda, “Development in 3D imagingprogram for small field-sized X-ray computed tomography (an Ortho-CTimage) for dentistry use”, Dentistry Radioactive Rays, 39 “4” P224–P229,2000.

However, there still remain lots of things in which the operator isinvolved to process the CT image in the conventional study. Therefore,it is desired that a system can semi-automatically create, based on thedata obtained by CT, a 3-dimensional model that the operator can processafterward.

Additionally, a CT image of the temporomandibular joint requires aplurality of transmitted images or reflective images taken by applyingradiation to a body. The amount of radiation applied to the body must beminimized so as to not expose the body to excessive radiation.

However, the CT image obtained by weak radiation is noisy. In the caseof a CT apparatus for the temporomandibular joint, the amount of X-rayradiation is limited to about 1/100 of that of a conventional CTapparatus for general medical use. The 3-dimensional image obtained bysuch a weak X-ray radiation sometimes is partially not clear enough fordental use.

DISCLOSURE OF INVENTION

Accordingly, it is a general object of the present invention to providea novel and useful image processing method in which one or more of theproblems described above are eliminated.

Another and more specific object of the present invention is to providea high speed image processing method to create a low noise image evenusing weak radiation.

To achieve this object, the present invention is configured to form a3-dimensional image of a 3-dimensional object by an extracting step forextracting a plurality of images of a 3-dimensional object along one ormore axes at different depths, said axes forming different angles withsaid 3-dimensional object, a binary coding step for eliminating a graylevel range containing small components of said object, remapping theother portion of the gray level range to a predetermined gray levelvalue range, and putting the plurality of images in binary form, and astep for forming a 3-dimensional image of said 3-dimensional objectbased on the plurality of images put in binary form by said binarycoding step.

To achieve this object, another configuration of the present inventionis configured to form a 3-dimensional image of a 3-dimensional object byan extracting step for extracting, from a 3-dimensional object, aplurality of images along a plurality of axes, wherein each axis is in adifferent direction and each image is at a different depth on an axis, abinary coding step for eliminating a gray level range containing smallcomponents of said object, remapping the other portion of the gray levelrange to a predetermined gray level value range, and putting theplurality of images in binary form, and a step for forming a3-dimensional image of said 3-dimensional object based on the pluralityof images put in binary form by said binary coding step.

The present invention, configured as described above, can provide animage processing method with which low noise images are obtained by highspeed processing even with a small amount of radiation, and one cansurely obtain an image of the 3-dimensional object even if a gray levelrange containing a small amount of the 3-dimensional object componentsis eliminated, since a plurality of images of different depths of the3-dimensional object are extracted for a plurality of angles and manyimages are obtained. Images with small influence of noise are alsoobtainable since noise components can be diffused by eliminating a graylevel range containing a small amount of the 3-dimensional objectcomponents, remapping the other portion to a predetermined gray levelrange, and binary coding.

In order to reduce the noise component without damaging the accuracy ofthe image, the present invention can be configured to include a step forextracting averaged images from the average of a predetermined number ofimages consecutive in the depth direction, or the present invention canbe configured to include a step of extracting images by averaging apredetermined number of images, each averaging performed on apredetermined number of images shifted by one image.

Further, in order to perform faster processing, the present inventioncan be configured to include a step for averaging one image for everyplurality of images and performing extraction.

In addition, the degrading of accuracy is negligible since the presentinvention uses a great number of images even when configured asdescribed above.

Furthermore, in order to reduce the noise component and extract theimage of the 3-dimensional object for sure, the present invention can beconfigured to include a step for binary coding after remapping a graylevel value range, in which noise component is negligible, in theoriginal gray level distribution containing a great amount of noisecomponents in the histogram of gray level of the 3-dimensional object.

Furthermore, in order to surely extract an image containing thecomponents of the 3-dimensional object from the image containing thebackground components or the imaging object component, the presentinvention can be configured to include a step for performing remappingdepending on a peak gray level, multiplied by a predeterminedcoefficient, in a gray level range containing a great amount of thebackground components or a peak gray level, multiplied by apredetermined coefficient, in a gray level range containing a greatamount of the 3-dimensional object and for binary coding.

Furthermore, in order to obtain the most suitable image in considerationof gray level distribution of the peripheral pixels, the presentinvention can be configured to include a step for statisticallyperforming the remapping based on the gray level distribution ofperipheral pixels and binary coding.

BRIEF DESCRIPTION OF DRAWINGS

Other objects, features, and advantages of the present invention willbecome more apparent from the following detailed description when readin conjunction with the accompanying drawings.

FIG. 1 is schematic block diagram of an imaging apparatus (an Ortho-CT).

FIG. 2 is a schematic diagram for explaining a method of imageextraction.

FIG. 3 is a photograph that shows an original picture image of atemporomandibular joint.

FIG. 4 is a flow diagram of image processing according to the firstembodiment.

FIG. 5 is a schematic diagram for explaining the averaging processaccording to the present invention.

FIG. 6 is a photograph that shows the image obtained by performing theaveraging process on the temporomandibular joint image showed in FIG. 3.

FIG. 7 is a histogram of a temporomandibular joint image.

FIG. 8 is a photograph that shows a temporomandibular joint image havinga histogram characteristic of FIG. 7.

FIG. 9 is a photograph that shows a temporomandibular joint image havinga histogram characteristic of FIG. 10.

FIG. 10 is a histogram in which gray levels 100–255 showed in FIG. 7 areremapped to gray levels 0–255 according to the first embodiment.

FIG. 11 is a binary coded image after partial emphasizing according tothe first embodiment.

FIG. 12 is a binary coded image of the original picture image accordingto the first embodiment.

FIG. 13 is a photograph that shows the 3-dimensional image which isformed based on the image provided by image processing according to thefirst embodiment.

FIG. 14 is a figure for explaining the angle of images about the 3 axesX, Y, and Z.

FIG. 15 is a diagram for explaining the processing procedure accordingto the second embodiment.

FIG. 16 is a diagram for explaining the angle of images about the fiveaxes.

FIG. 17 shows regular polyhedrons.

FIG. 18 is a photograph that shows the 3-dimensional image that isobtained by conventional image processing.

FIG. 19 is a photograph that shows the 3-dimensional image that isobtained by the first embodiment.

FIG. 20 is a photograph that shows the 3-dimensional image that isobtained by image processing in 3 directions according to the secondembodiment.

Main reference marks used in the above figures are explained as follows.

1 is a system; 11 is a radiation source; 12 is a detector; 13 is ananalog-to-digital converter; 14 is a general purpose computer; and 21 isan imaged 3-dimensional object.

BEST MODE FOR CARRYING OUT THE INVENTION

A description of the preferred embodiment of the present invention willbe given below.

THE FIRST EMBODIMENT

This embodiment is the case such that 2-dimensional image data of the3-dimensional object are extracted from 3-dimensional data obtained byan Ortho-CT apparatus.

FIG. 1 is a block diagram showing an Ortho-CT apparatus.

Imaging apparatus 1 is an Ortho-CT apparatus configured by a radiationsource 11, detector 12, analog-to-digital converter 13, and a generalpurpose computer 14. Radiation source 11 emits radiation, and theradiation emitted by radiation source 11 irradiates the 3-dimensionalobject 21. The radiation is transmitted through the 3-dimensional object21 and is incident on detector 12. Detector 12 outputs detection signalsin response to the strength of the incident radiation.

In addition, general purpose computer 14 may perform image processing byinstalling and running an image processing program stored in recordingmedia such as HDD, CD-ROM, CD-R, and FDD.

Additionally, general purpose computer 14 may operate as theanalog-to-digital converter 13 by running a software program. In thiscase, a separate analog-to-digital converter 13 may not be required.

Radiation source 11 and detector 12 are positioned facing each otherwith the 3-dimensional object 21 in between, and can rotate around theZ-axis at least 180 degrees. The (analog) signal detected by detector 12is provided to analog-to-digital converter 13, and converted intodigital data. The data that are converted by analog-to-digital converter13 are provided to general purpose computer 14 for image processing. The3-dimensional data of the 3-dimensional object 21 are obtained in thismanner. As showed in FIG. 2, the 3-dimensional projection data obtainedby the Ortho-CT has a cylinder shaped imaging region of 240*300*300pixels.

In the image processing according to this embodiment, 3-dimensional dataare first converted into 2-dimensional data, and then converted againinto 3-dimensional data so that the image processing becomes simple. Inother words, general purpose computer 14 extracts a 2-dimensional imagefrom cylinder-shaped 3-dimensional data directly provided by anOrtho-CT. General purpose computer 14 processes the 2-dimensional image,the details of which will be described later, to obtain binary imageswith reduced noise and converts the binary images into 3-dimensionaldata again.

An Ortho-CT is described in detail in “Development of Ortho Cubic SuperHigh Resolution CT (Ortho-CT)”, Car '98, P780–P785 (proc.), 1998,written by Arai Y, Tammisalo E, Iwai K, et al.

Next, the method for extracting 2-dimensional images from 3-dimensionaldata directly provided by an Ortho-CT will be described.

FIG. 2 is a figure convenient for explaining the image extraction methodaccording to an embodiment of the present invention.

Three dimensional data are obtained by taking images of thetemporomandibular joint with the Ortho-CT showed in FIG. 1.Two-dimensional images are obtained (extracted) from the 3-dimensionaldata. In order to get a temporomandibular joint image with a relativelyclear outline, 4,416 images (276 images×16 directions), for example, aretaken. In other words, as shown in FIG. 2, 276 images of 300 pixelswide×240 pixels long, each having different depth in a direction, aretaken for 16 directions. In addition, each pixel of extracted image datais expressed in 8 bits, 256 steps of gray scale, for example.

FIG. 3 is an original image of a temporomandibular joint extracted asshowed in FIG. 2. The original image showed in FIG. 3 is noisy sinceimage processing has not been performed yet.

General purpose computer 14 extracts the 2-dimensional images from the3-dimensional data detected by detector 12 as described above, andstores the 2-dimensional images in internal memory. Image processing ofthe present embodiment is performed using 276 two-dimensional imagesstored in internal memory.

A detailed description of the image processing according to the presentembodiment will be given below.

FIG. 4 is a flow diagram showing the image processing according to thefirst embodiment of the present invention.

Image processing of the first embodiment includes steps S1 through S4.Step S1 is a step for averaging a plurality of images in order to reducenoise. Step S2 is a step for appropriately remapping the plurality ofimages that were averaged in Step S2. In step S2, one may use ahistogram emphasis method, for example. Step S3 is a step for binarycoding an image mapped in step S2. Step S4 is a step for forming a3-dimensional image of the imaging object based on the image binarycoded in step S2.

By the way, a clear 2-dimensional image having low noise is available instep S3 since the image data are processed in steps S1 and S2 prior tostep S3. Therefore, one may regard steps S1 and S2 as preparatory stepsof step S3 included therein.

Averaging processing in step S1 will be described first. FIG. 5 is afigure convenient for explaining the operation of averaging processingaccording to the embodiment of the present invention.

As showed in FIG. 5(A), the averaging processing forms one 2-dimensionalimage by averaging every eight 2-dimensional images, for example, eachpixel of the one 2-dimensional image being the average of correspondingpixels of the eight 2-dimensional images. For example, the first screenP1 is the average of eight screens p1–p8; the second screen P2 is theaverage of eight screens p2–p9; and the third screen P3 is the averageof eight screens p3–p10.

FIG. 6 is a figure showing the averaged picture of the temporomandibularjoint image shown in FIG. 5.

Since noise is random, the noise approaches a certain value when it isadded to each other. As showed in FIG. 6, one may notice that the noiseis reduced by averaging processing.

As described above, one can reduce the noise by only the averagingprocessing, even without any special image processing technique. In thisembodiment, the next combination of eight screens for averagingprocessing is shifted by one screen from the preceding combination ofeight screens, but one can select the next combination of eight screensnext to the preceding combination of eight screens as shown in FIG.5(B). Additionally, the number of screens that are averaged is notlimited to eight. Furthermore, averaging is not limited to a simplemean, but can be another statistical process that can reproduce imageswithout distortion. For example, one can use an arithmetic meandepending on the characteristics of the noise.

Next, emphasizing processing in compliance with the histogram emphasismethod of step S2 is executed.

The histogram emphasis method emphasizes only the histograms in the graylevel between a–b by applying the following expression (1) to an image.Y=255*(X−a)/(b−a)  (1)

In this embodiment, some density ranges are emphasized first. Forexample, a gray scale range (a, b) that includes a large portion of theoutline of temporomandibular joint, and then, the gray scale range a–bis remapped to the gray scale range 0–255, and accordingly partialemphasis is made.

In addition, the details of the histogram emphasis method are describedin “Introduction to image processing by C language, Shoko-Do, 2000”.

In consideration of histograms of the gray level of an image, an imagesuitable for binary coding generally has 2 peaks. That is, the imagesuitable for binary coding is an image such that, when binary coded, anobject and a non-object form respective peaks, of which difference isclear. This situation is desirable.

Histogram emphasizing will be described in more detail as follows.

FIG. 7 is a histogram of gray levels of a temporomandibular joint image.Histogram emphasis processing reduces influence of noise components byremoving gray level ranges that do not include the gray level of thetemporomandibular joint that is an object (the range that mainlyincludes noise components and does not include many components oftemporomandibular joint) and remapping the other gray level range to theoriginal range.

In a temporomandibular joint image shown in FIG. 7, as the result oftrial and error, it is known that the gray level range less than 100,for example, does not include much information of the temporomandibularjoint. Therefore, the histogram emphasis processing reduces noise byeliminating the gray level range less than 100 and remapping theremaining range 100–255 to the original range to emphasize the graylevel range 100–255, and creates images with reduced effect of noise.

In addition, even if the histogram emphasis processing cuts a portion ofuncertain data, the cut causes no problem in forming a 3-dimensionalimage of temporomandibular joint. In other words, since the presentinvention treats a lot of temporomandibular joint images and, even ifthe present invention cuts a portion of uncertain data,temporomandibular joint images of the other angles indicate the portionclearly, and there is no problem in forming the temporomandibular jointimage. For example, since an unclear region that is located at the edgeof a certain image is, in the case of another image of a differentangle, located at the center, ignoring the unclear data at the edgeposition causes no problem.

FIG. 8 is an original image of a temporomandibular joint havinghistogram characteristics shown in FIG. 7; FIG. 9 is a temporomandibularjoint image of which gray levels less than 100 are eliminated and graylevels 100–255 are emphasized; and FIG. 10 is a histogram of atemporomandibular joint image of FIG. 9. As to the image afteremphasizing shown in FIG. 9, in comparison with the original image shownin FIG. 8, one may notice that the noise component is reduced, and thecontrast between the portion of the temporomandibular joint and thebackground is clear.

In addition, as to mapping, various kinds of methods are considerable.In the gray level histogram shown in FIG. 7, among three peaks, it issupposed that the left peak is the black level, the middle is thebackground level, and the right is the white level. Thus, since there isno problem for the temporomandibular joint image information even if thebackground portion in the center does not exist, for example, one caneliminate the background portion in the center and use gray levels nearthe white level and the black level for emphasizing.

Furthermore, one may perform remapping based on, in the histogram ofdensity shown in FIG. 7, the peak density of a density range thatincludes a lot of the background components or the peak density of adensity range that include a lot of the 3-dimensional object componentsmultiplied by a predetermined coefficient.

Moreover, one may select, from the peripheral density distribution, thedensity range by which the best image is statistically available as thedensity range of remapping, and the important thing is to remap in thedensity range where the influence of noise can be reduced.

Following step 2, in step S3, binary coding processing is performed bythe Canny method, for example. The binary coding processing by the Cannymethod of step S3 will be explained next.

The binary coding processing by the Canny method is binary codingprocessing that detects an edge by obtaining a local maximum of agradient of the image.

At first, using 2 thresholds, the binary coding processing detects astrong edge and a weak edge. And, only in the case such that the weakedge is connected to the strong edge, the binary coding processing putsthe weak edge in binary form by including the weak edge in an outputedge. In addition, the details of the Canny method are indicated in“CANNY, A Computational Approach to Edge Detection, IEEE TRANSACTION ONPATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1986”.

Additionally, depending on an extraction image portion, one may set thethreshold at an appropriate value at which the best image is empiricallyobtained. Furthermore, binary coding processing is not limited to theCanny method, and one can binary code using another binary codingmethod.

Additionally, the emphasized image shown in FIG. 9 is binary coded bythe Canny method, which is the binary coded image shown in FIG. 11, andthe original image showed in FIG. 6 is binary coded “as is” by the Cannymethod, which is the binary coded image showed in FIG. 12. According tothese binary coded images, in the binary coded image showed in FIG. 11,in comparison with the binary coded image shown in FIG. 12, it isevident that noise is reduced. Even in FIG. 12, the outline of thetemporomandibular joint appears, but, it is evident that the image isnoisier. This indicates that, in the histogram of the original imageshown in FIG. 7, there exist a plurality of mountains, and the noisecomponent is not concentrated on a certain gray level, but isdistributed in the gray levels 100–255 too.

In this embodiment, by mapping the gray levels 100–255 in the gray level0–255 by the histogram emphasizing method, one can scatter the noisecomponent existing in the gray level 100–255, and reduce the frequencyof detecting a noise having a small gradient as an edge in the case ofbinary coding by the Canny method, and, as a result, one can furtherreduce the noise component.

As described above, one can effectively reduce noise by binary codingthe image by the Canny method after performing the histogram emphasismethod.

As described above, one can obtain a clear 3-dimensional image byforming the 3-dimensional image using binary coded 2-dimensional images.In addition, a 3-dimensional image can be formed, using the oppositealgorithm for the cutting of an image, for example.

In FIG. 13 is the 3-dimensional image formed based on an image providedby the above image processing.

According to the present embodiment, it is possible to obtain a clear3-dimensional image from noisy 2-dimensional images taken with a smallamount of radiation exposure. In this embodiment, a plurality of imagesprocessed by averaging processing are processed by the histogramemphasizing method and binary coded, so that one can lower the influenceof noise and detect an edge without performing complicated processing.That is how one can minimize the influence of noise.

In addition, in this embodiment, the same processing is performed on4,416 2-dimensional images, but it is possible to apply, depending onthe extracted portion, averaging processing and binary coding processingthat are most suitable for the portion. For example, one may obtainextraction results with varying thresholds and switching thresholds,depending on the portion of images by setting a threshold for eachportion with which the best image is obtained.

Moreover, in this embodiment, an example wherein the present inventionis applied to the temporomandibular joint is explained, but the imageprocessing according to the present invention is not limited to thetemporomandibular joint, but applicable to an imaging object thatrequires imaging with little radiation due to a problem of radiationexposure.

Moreover, the image processing method according to this embodiment cutsimages of various angles around the Z-axis, and performs interpolationon edges that cannot be obtained in a direction.

It is also possible to cut images of various angles around the X-axis orY-axis instead of the Z-axis.

Moreover, it is possible to cut images around:

(i) X-axis and Y-axis that are rotated by 45 degrees around the originin the XY plane;

(ii) Y-axis and Z-axis that are rotated by 45 degrees around the originin the YZ plane; and

(iii) Z-axis and X-axis that are rotated by 45 degrees around the originin the ZX plane.

Moreover, it is possible to cut images in various angles around aplurality of axes.

THE SECOND EMBODIMENT

By the way, as to the image processing method described above, as showedin FIG. 14, since the image processing is applied to many images takenin various directions around the Z-axis, for example, there exists aproblem that the image processing takes a long time.

For example, when the present inventors measured the time required forprocessing in the case of 4,416 images in total, that is, 276 images perdirection, 16 directions, using a computer operated at 800 MHz andhaving 256 MB RAM memory, for example, it took 15 minutes for cuttingimages, 35 minutes for image processing and forming, and 5 minutes fordisplaying in 3-dimension, in total 55 minutes. It is said that themaximum time that can be spent for clinical application is about 10minutes, and the use of the image processing method is not practical.

Therefore, in the embodiment described above, processing in variousdirections around one axis is performed 3-dimensionally, but in thesecond embodiment as shown in FIG. 15, 2-dimensional images (B) in eachdirection of X-axis, Y-axis, and Z-axis, 3 directions in total, are cutfrom 3-dimensional data (A) provided by means of the imaging apparatus.The 2-dimensional images that are cut are processed by the averagingprocessing, histogram emphasizing, and binary coding processing (C) asshown in FIG. 4. Using these 2-dimensional images provided,3-dimensional images are formed and stored in the internal memory (D).The 3-dimensional image stored in the internal memory is displayed. Inaddition, in this embodiment, besides the cutting of an image, the imageprocessing method according to the first embodiment described above canbe used.

In addition, as to the above embodiment, the case such that images arecut from 3 directions of X-, Y- and Z-axes is described, but even a caseother than this can be embodied.

For example, one may add a part or all of the following axes to X-, Y-,and Z-axes described previously:

(i) X-axis and Y-axis rotated 45 degrees in the XY plane around theorigin;

(ii) Y-axis and Z-axis rotated 45 degrees in the YZ plane around theorigin; and

(iii) Z-axis and X-axis rotated 45 degrees in the ZX plane around theorigin and X-axis.

For example, in FIG. 16, an example such that images are cut in thedirections of five axes consisting of X′ axis and Y′ axis that arerotated by 45 degrees in the XY plane in addition to X-, Y- and Z-axes.

Moreover, it is possible to use, as an axis, a part or all of the linesconnecting the center of each face of regular polyhedrons and the centerof the regular polyhedrons, regular tetrahedron (A), regular hexahedron(B), regular octahedron (C), regular dodecahedron (D), and regularicosahedron (E), as showed in FIG. 17.

A 3-dimensional image processed in 3 directions actually using themethod showed in FIG. 15 is showed in FIG. 20. A 3-dimensional imageobtained by a conventional method is showed in FIG. 18, and a3-dimensional image obtained by the method of the first embodiment isshowed in FIG. 19. The 3-dimensional image showed in FIG. 20, incomparison with the 3-dimensional image showed in FIG. 19, is a3-dimensional image in which some noises are observable but the shape ofthe object is recognizable. Moreover, the 3-dimensional image showed inFIG. 20, in comparison with the 3-dimensional image showed in FIG. 18,is a 3-dimensional image in which no deficit exists and the shape of theobject is recognizable. This situation is understandable.

Moreover, as to processing time, the method of the second embodimenttook about 14 minutes from the reading of 3-dimensional projection datato the completion of a file making. In comparison with that it takesabout 55 minutes in the case of the conventional 16-directionprocessing, so a 3-dimensional image can be obtained in about one forthof the time with the second embodiment.

As described above, as to the image showed in FIG. 20, in comparisonwith the 3-dimensional image of 16 directions, a portion that isinferior to the 3-dimensional image of 16 directions is more or lessobservable, but the 3-dimensional image obtained by image processing in3 directions is considered to be effective in consideration ofcomputation time, as long as the objective is grasping shapes.

As described above, as to the first and the second embodiments, sincemany images are obtained by extracting them, in a plurality of angles oraround one or more axes, from a plurality of images having differentdepths of a 3-dimensional object, it is possible to surely obtain animage of the imaging object even if a density range containing a fewcomponents ob the 3-dimensional object is eliminated, and, since noisecomponents can be diffused by eliminating the density ranges containinga few components of the 3-dimensional object, remapping the otherportion to a predetermined density range, and binary coding the firstand the second embodiments are characterized in that, for example,images having less influence of noise are obtainable.

Moreover, the first and the second embodiments are characterized inthat, for example, images having less influence of noise can be obtainedsince they can reduce noise components without damaging the accuracy ofimages by extracting averaged images from the average of a predeterminednumber of images consecutive in the direction of depth.

Moreover, the first and the second embodiments are characterized in thatthey can obtain images having less influence of noise since they canreduce noise components without losing the accuracy of images byaveraging and extracting images while shifting by one image in thedirection of depth for averaging.

Moreover, the present invention can perform image processing at a highspeed by averaging a plurality of images and extracting one image by theplurality of images, and then, the present invention is characterized inthat, for example, it can reduce the loss in accuracy by treating manyimages that are imaged.

Moreover, the first and the second embodiments, in the case of mapping,can reduce noise without including noise components and surely extractimages of the 3-dimensional object by selecting, as a threshold, adensity in the density distribution containing great noise components inthe histogram of density level of the images of the 3-dimensionalobject, at which the noise component is negligible.

Moreover, the first and the second embodiments can surely extract thedensity component containing the background components or the3-dimensional object components by performing remapping based on thepeak density of the density range containing the background componentsor the peak density of the density range containing the 3-dimensionalobject components, both multiplied by a predetermined coefficient.

Moreover, the first and the second embodiments are characterized in thatthey can provide the most suitable image for forming a 3-dimensionalimage since a threshold with which the statistically optimum image isobtained is selected.

Moreover, it is possible that, in the general purpose computer 14 showedin FIG. 1, a computer program that causes the general purpose computer14 to perform an extraction step to extract a plurality of images havingdifferent depths of a 3-dimensional object around one or more axes overa plurality of angles, a binary coding step that eliminates the densityrange of the plurality of images obtained in the extraction step, inwhich few components of the object are contained, and remaps the otherportion to a certain density range, and puts the image in a binary form,and a step to form a 3-dimensional image of the 3-dimensional objectbased on the images put in binary form in the binary coding step, andimage processing according to the first and second embodiments can beperformed by this program.

In addition, needless to say, the present invention is not limited tothe above embodiments and, without deviating from the scope of claims,various variations and modifications are possible.

1. An image processing method, comprising: extracting a plurality ofimages from 3-dimensional data of a 3-dimensional object along one ormore axes at different depths, said axes forming an angle with eachother; eliminating a gray level range including at least two componentsof said 3-dimensional object from the plurality of images extracted, andremapping remaining gray level range to a predetermined gray levelrange, thereby forming a plurality of binary images; and forming a3-dimensional image of said 3-dimensional object based on the pluralityof binary images.
 2. An image processing method comprising: extracting,from 3-dimensional data of a 3-dimensional object, a plurality of imagesalong a plurality of axes, wherein each axis is in a different directionand each image is at a different depth on an axis; eliminating a graylevel range including at least two components of said 3-dimensionalobject from the plurality of images extracted in said extracting, andremapping remaining gray level range to a predetermined gray levelrange, thereby forming a plurality of binary images; and forming a3-dimensional image of said 3-dimensional object based on the pluralityof binary images formed in said binary coding.
 3. The image processingmethod claimed in claim 1, wherein the eliminating and remapping furthercomprise: averaging subsets of the plurality of images to form averageimages, the subsets including a predetermined number of successiveimages along one of the at least one axes.
 4. The image processingmethod as claimed in claim 3, wherein the averaging is performed for aparticular subset to form one of the average images, and then for a nextsubset including a next one of the plurality of images that was not yetincluded in the particular subset, and excluding the last one of theplurality of images that has already included in the particular subset.5. The image processing method as claimed in claim 3, wherein theaveraging is performed for a particular subset to form one of theaverage images, and subsequently is performed for a next subset ahead ofthe particular subset by a predetermined number of images.
 6. The imageprocessing method as claimed in claim 1, wherein the eliminating andremapping further comprise: remapping, in a gray level histogram of the3-dimensional object including great noise component, a gray level rangeincluding negligible noise component to thereby form a plurality ofbinary images.
 7. The image processing method as claimed in claim 1,wherein in the eliminating and remapping, the remapping is performedbased on a peak gray level multiplied by a predetermined coefficient ina gray level histogram of the 3-dimensional object including asubstantial background component, or in a gray level histogram of the3-dimensional object including a substantial component of the3-dimensional object.
 8. The image processing method as claimed in claim1, wherein the eliminating and remapping further comprise: statisticallyremapping on the basis gray level distribution of peripheral pixels. 9.A computer readable recording medium storing an image processing programthat causes a computer, when executed by the computer, to perform:extracting a plurality of images from 3-dimensional data of a3-dimensional object along one or more axes at different depths, saidaxes forming an angle with each other; eliminating a gray level rangeincluding at least two components of said 3-dimensional object from theplurality of images extracted, and remapping remaining gray scale rangeto a predetermined gray level range, thereby forming a plurality ofbinary images; and forming a 3-dimensional image of said 3-dimensionalobject based on the plurality of binary images.